Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/devkindhq/ideogram-ai-toolkit/logo-promptingnpx skills add devkindhq/ideogram-ai-toolkit --skill logo-promptinggit clone --depth 1 https://github.com/devkindhq/ideogram-ai-toolkitWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/devkindhq/ideogram-ai-toolkit/logo-prompting)<a href="https://agentmods.dev/skills/devkindhq/ideogram-ai-toolkit/logo-prompting"><img src="https://agentmods.dev/badge/skills/devkindhq/ideogram-ai-toolkit/logo-prompting.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00169 | $0.01792 |
| Opus 5 | $0.00084 | $0.00896 |
| Sonnet 5 | $0.00034 | $0.00358 |
| Haiku 4.5 | $0.00017 | $0.00179 |
Grade A, and why
logo-prompting scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Logo Prompting
Writing a good logo prompt is a compression problem: you're translating a brand's strategy and voice into a short, literal, image-model-legible spec — while actively fighting the model's own priors, which default to AI-startup visual clichés (gradients, neural nodes, glowing orbs, soundwave bars) unless explicitly told not to.
This skill exists because a logo prompt has two failure modes, and they pull in opposite directions:
- Too vague → the model falls back to generic SaaS-logo training-data averages.
- Too literal / over-specified → the model draws exactly the clichéd icon you named (a phone for a calling app, a shield for security, a lightbulb for ideas) instead of an abstract mark that earns its meaning.
Good logo prompting threads between these: specific about constraints (palette, type, mood, what to avoid), abstract about the mark itself (suggest, don't depict).
Before writing a prompt: gather the four inputs
Don't start typing a prompt cold. Pull these four things first — they're the actual inputs, the prompt is just their compressed form:
- Brand truth — read the project's
brand.mdif one exists (Strategy + Voice + Visual layers). If there's nobrand.md, ask for the equivalent: what the brand is, what it's not, the archetype, the core promise. A logo prompt with no brand truth behind it is decoration, not identity. - The Visual layer specifically — Colors (exact hex, named tokens, and their usage rule — which color is primary vs. reserved-for-one-state), Typography (display face + weight + roman-only), Style keywords + reference brands. See
references/brand-visual-layer.md. - Intake context — what is this mark actually for (wordmark, icon-only, favicon, both), what does it need to survive (16px favicon, dark mode, embroidery, print), who reads it (a shift worker at 2am glancing at a phone screen reads differently than a landing-page visitor). See
references/logo-offer-questions.mdfor the fuller intake list — use it as an interview checklist when the user hasn't already supplied this. - The anti-slop guardrails — the specific clichés this brand must never look like (see
references/anti-slop-discipline.md). Every brand has its own version of "don't look like a generic AI startup" — for a fintech it might be "no padlock icon," for a care-work app it's "no clinical cross," etc. Name the negatives explicitly; a model that isn't told what to avoid will reach for it.
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- examples/fleetline-four-directions.md 6.8 KB
- examples/images/fleetline-01-wordmark.webp 2.7 KB
- examples/images/fleetline-02-badge-emblem.webp 4.5 KB
- examples/images/fleetline-03-mark-only.webp 2.3 KB
- examples/images/fleetline-04-monospace-identity.webp 1.7 KB
- references/anti-slop-discipline.md 4.4 KB
- references/brand-visual-layer.md 3.3 KB
- references/logo-offer-questions.md 2.7 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 66 lines · 169 tokens per session scan A e05d04085d5f
logo-prompting is a skill published in the GitHub repository devkindhq/ideogram-ai-toolkit (2 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 169 tokens to every session and 1,792 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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